In this paper we present an alternative way of modeling speaker information, that is largely inspired by style-transfer [1]}. This seminal algorithm in computer vision is capable of producing new images that combine the content of an image with the style of other images or artworks. The style of an image is modeled by the correlations between the channels of the convolutional neural network. By considering styles as broadly analogous to speakers, we experiment with modeling speakers by channel-wise correlation. This is in contrast to averaging along the time dimension of each channel frequency pair of the output tensor that is used in statistical pooling. The other sources of variability that can also be considered as style of the recording, such as noise and session effectsSession effects are related to the recording device and they are also known as channel effects in speaker recognition literature. are suppressed by training the model end-to-end using supervised learning with speakers as targets. The resulting embeddings are highly speaker-discriminative, yielding state-of-the-art results on VoxCeleb using plain cosine similarity.
